An Interaction-Pattern-Based Approach to Prevent Performance Degradation of Fault Detection in Service Robot Software

In component-based robot software, it is crucial to monitor software faults and deal with them on time before they lead to critical failures. The main causes of software failures include limited resources, component-interoperation mismatches, and internal errors of components. Message-sniffing is one of the popular methods to monitor black-box components and handle these types of faults during runtime. However, this method normally causes some performance problems of the target software system because the fault monitoring and detection process consumes a significant amount of resources of the target system. There are three types of overheads that cause the performance degradation problems: frequent monitoring, transmission of a large amount of monitoring-data, and the processing time for fault analysis. In this paper, we propose an interaction-pattern-based approach to reduce the performance degradation caused by fault monitoring and detection in component-based service robot software. The core idea of this approach is to minimize the number of messages to monitor and analyze in detecting faults. Message exchanges are formalized as interaction patterns which are commonly observed in robot software. In addition, important messages that need to be monitored are identified in each of the interaction patterns. An automatic interaction pattern-identification method is also developed. To prove the effectiveness of our approach, we have conducted a performance simulation. We are also currently applying our approach to silver-care robot systems.